1Threshold friction speed is an important factor for assessing the soil erodibility, but its measurement is time consuming and costly. Estimating threshold friction velocity by use of soil reflectance increases operating speed and reduces cost. The aim of this study was to compare the efficiency and accuracy of partial least squares regression (PLSR), support vector regression (SVR) and artificial neural network (ANN) regression models in estimating the threshold friction velocity in dust-prone areas of Khuzestan Province. For this purpose, 91 soil samples were collected from the central and southern parts of dust-prone areas of the province and the threshold friction velocity was determined using wind tunnels. Then, the reflectance spectra of soil samples were obtained with a spectrometer. Pre-processing methods were performed on the main spectrum and modeling was performed using, PLSR, SVR and ANN models. The results showed that the threshold friction velocity in the region was 9. 7 m/s and the minimum was 5. 25 m/s. Also, the threshold friction velocity was significantly (p<0. 05) correlated with dissolved sodium (r=-0. 58) and sodium adsorption ratio (R=-0. 48). The ANN model had the best estimation accuracy in the second derivative preprocessing (PRD = 2. 52) and the SVR model had the lowest estimation accuracy in the main spectrum (PRD = 0. 56). Finally, the key wavelength of the threshold friction velocity was in the range of 1850 and 1930 nm. Because of the soil reflectance correlation with threshold friction velocity (r=0. 76), the spectroscopy method can be used to assess the soil erodibility in areas prone to dust production.